Reading

CIFTI files organize the gray matter of the brain into “greyordinates”: vertices representing the left and right cortical surfaces, and voxels representing the subcortical gray matter structures and the cerebellum. A CIFTI file consists of two parts: (1) a NIFTI XML header which contains all the metadata including medial wall locations, subcortical structure labels, and the subcortical volumetric mask; and (2) a matrix representing all the greyordinate data. These components are read in together with read_cifti:

basename(cifti_fnames["dscalar"])

## [1] "Conte69.MyelinAndCorrThickness.6k_fs_LR.dscalar.nii"

xii <- read_xifti(cifti_fnames["dscalar"])
xii

## Brain Structures: left cortex, right cortex  
##  left cortex: 5412 surface vertices, 2 measurements.
##  right cortex: 5434 surface vertices, 2 measurements.
## Intent: 3006 (dscalar).

#plot(xii) # for visualization, we'll get to that next!

By default, read_cifti only reads in the left and right cortex data. The subcortical data can be included by using the argument brainstructures="all". Other brainstructure combinations can be specified too, e.g. brainstructures=c("left", "subcortical"). The full set of choices for brainstructures is any combination of "left", "right" and "subcortical", or "all" for all three.

The resulting object produced by read_cifti is a "xifti" with components data (the greyordinate data matrix, separated by brainstructure), meta (metadata, most of which is from the NIFTI XML header), and surf (surface geometry). The last component distinguishes a "xifti" from a CIFTI: the left and right cortical surface geometries are not included in CIFTI files, so they must be read from separate surface GIFTI files (ending in surf.gii). The surface must be compatible: the number of vertices must be the same, and each vertex in the CIFTI data must correspond to the vertex location in the corresponding GIFTI surface file. In this way, a "xifti" represents a combination of a CIFTI file with compatible GIFTI files for the cortical mesh.

We can add GIFTI surface geometries like so:

basename(cifti_fnames["dtseries"])

## [1] "Conte69.MyelinAndCorrThickness.32k_fs_LR.dtseries.nii"

xii <- read_xifti(cifti_fnames["dtseries"])
xii <- add_surf(xii, surfL=surfL_fname, surfR=surfR_fname)
xii

## Brain Structures: left cortex, right cortex  
##  left cortex: 30424 surface vertices, 2 measurements.
##      left surface model is present.
##  right cortex: 30527 surface vertices, 2 measurements.
##      right surface model is present.
## Intent: 3002 (dtseries).

Alternatively, we could have provided the surface geometries at the outset of reading the CIFTI file:

xii2 <- read_xifti(cifti_fnames["dtseries"], surfL_fname=surfL_fname, surfR_fname=surfR_fname)
all.equal(xii, xii2) # same result

## [1] TRUE

To only read the CIFTI header, use info_cifti.

xii_info <- ciftiTools::info_cifti(cifti_fnames["dscalar"])
str(xii_info) # shows header structure

## List of 3
##  $ cortex :List of 2
##   ..$ medial_wall_mask:List of 2
##   .. ..$ left : logi [1:5762] TRUE TRUE TRUE TRUE TRUE TRUE ...
##   .. ..$ right: logi [1:5762] TRUE TRUE TRUE TRUE TRUE TRUE ...
##   ..$ resamp_res      : NULL
##  $ subcort:List of 3
##   ..$ labels   : NULL
##   ..$ mask     : NULL
##   ..$ trans_mat: NULL
##  $ cifti  :List of 4
##   ..$ intent         : num 3006
##   ..$ brainstructures: chr [1:2] "left" "right"
##   ..$ names          : chr [1:2] "MyelinMap_BC_decurv" "corrThickness"
##   ..$ misc           :List of 4
##   .. ..$ ParentProvenance : chr "C:\\Users\\damon\\AppData\\Local\\Temp\\Rtmp2X6THF/resampled_6000_Conte69.MyelinAndCorrThickness.32k_fs_LR.L.fu"| __truncated__
##   .. ..$ ProgramProvenance: chr "Connectome Workbench\nType: Command Line Application\nVersion: 1.4.2\nQt Compiled Version: 5.7.0\nQt Runtime Ve"| __truncated__
##   .. ..$ Provenance       : chr "C:\\Users\\damon\\Desktop\\WORKBE~1\\BIN_WI~1\\WB_COM~1.EXE -cifti-create-dense-scalar C:\\Users\\damon\\Deskto"| __truncated__
##   .. ..$ WorkingDirectory : chr "C:/Users/damon/Desktop/ciftiTools/vignettes"

Writing

When "xifti" object is written to files, the CIFTI components are placed in a CIFTI file and the surface geometries, if any, are placed in GIFTI files.

out_dir <- "output"

write_xifti(
  xii, file.path(out_dir, "my_cifti.dtseries.nii"), 
  file.path(out_dir, "my_L.surf.gii"), file.path(out_dir, "my_R.surf.gii")
)

## Writing left cortex.
## Writing right cortex.
## Creating CIFTI file from separated components.
## Writing surface geometry GIFTI(s).

With separate_cifti, a CIFTI can be separated and written into its component parts: the cortical data can be written to GIFTI metric files, and the subcortical data can be written to a NIFTI file. In addition, any ROIs or labels will also be written to files. The files are automatically named unless a new file name is provided.

# Use default names for everything except left cortex
separated_fnames = separate_cifti(
  cifti_fnames["dscalar_ones"], brainstructures="all", 
  cortexL_fname="my_left_cortex.func.gii", write_dir = out_dir
)
# Files written to `out_dir`, or current working dir. if not specified
basename(separated_fnames)

## [1] "my_left_cortex.func.gii" "ones_1k.ROI_L.func.gii" 
## [3] "ones_1k.R.func.gii"      "ones_1k.ROI_R.func.gii" 
## [5] "ones_1k.nii"             "ones_1k.labels.nii"     
## [7] "ones_1k.ROI.nii"

Separated files can be read into R with the oro.nifti/RNifti and gifti packages, and combined into a "xifti" object with as.xifti.

Surface visualization

view_xifti_surface(xii) displays the cortical data on the surface mesh. This function has several primary arguments:

• color_mode specifies the nature of the data values: "sequential", "qualitative" and "diverging". If it is not provided, a default mode that makes sense for the data will be used.
• colors specifies the color palette to use. If it is not provided, a default palette that makes sense for the color_mode is used.
• idx controls which column(s) to display.
• widget and fname control the output type. If fname is not provided, an interactive plot is created: by default, an Open GL window if the length of idx is one, and an embedded htmlwidget if the length of idx is greater than one. widget can be used to override this default. On the other hand, if fname is provided, static image (png) files for each idx are created, unless fname ends in .html in which case a separate, interactive html file will be saved. Lastly, Open GL windows can be embedded in R Markdown documents for knitting; refer to the source code of this vignette to see how this works.
• surfL and surfR specify the surface geometry to plot the data on. If not provided, the “inflated” surfaces included in ciftiTools are used. The default surface can also be changed to “very inflated” or “midthickness” by e.g. ciftiTools.setOption("surf", "very inflated").

Let’s see an example using each color_mode option. Note how the included surfaces are used in the first plot, but if none are present as in the second and third plots, the default surfaces are automatically used for visualization. We’ll also make the second plot interactive by requesting display of two idx. Try clicking and dragging around the second plot to rotate, and scrolling to zoom in and out.

# Normally `cex.title` doesn't need to be set, as it defaults to a good choice.
#   But when knitting static images this way, the default becomes a bit too big
#   based on how knitting works.
view_xifti_surface(xii, idx=1, zlim=c(1,2), title='color_mode = "sequential"', cex.title=1.3)

dtseries file; first column; sequential palette

xii <- read_cifti(cifti_fnames["dscalar"]) # no GIFTI included, so the default inflated surface is used.
view_xifti_surface(
  xii, idx=1:2, zlim=c(0,5), color_mode = "diverging",
  title='color_mode = "diverging"', cex.title=1.3
)

dlabel <- view_xifti_surface(
  read_cifti(cifti_fnames["dlabel"]), 
  # Interactively, a color legend that displays the label names will also be printed.
  legend_ncol=5, 
  title='color_mode = "qualitative"', cex.title=1.3
)

dlabel file; first label; palette from label metadata

(Note that the dtseries used in this example does not truly contain fMRI BOLD timeseries data, but we use it for illustration.)

Volume visualization

view_xifti_volume(xii) displays the subcortical data in slices. To view interactively in a web browser, set interactive=TRUE. By default, a series of slices is displayed overlaid on the MNI template. The orientation, numbers of slices, index and value range can be adjusted.

# cifti_fnames["dscalar_ones"] is the only file with subcortical data
xii <- read_cifti(cifti_fnames["dscalar_ones"], brainstructures="subcortical")
view_xifti_volume(xii)

## Values to be plotted range from 1 to 1.

Subcortical data (all ones)

# For information only, since papaya viewer cannot be opened during knitting
view_xifti_volume(xii, interactive = TRUE)

The S3 method plot(xii) will display the cortical data if possible, and the subcortical data otherwise.

Resampling

ciftiTools can resample CIFTI files to a lower resolution. Here, we resample the 32k dtseries file to 6k vertices. We also provide the surfaces and resample them in conjunction.

resampled_xii_fname <- "my_new_resampled.dtseries.nii"
resampled_surfL_fname <- "my_resampled_surfL.surf.gii"
resampled_surfR_fname <- "my_resampled_surfR.surf.gii"
  
xii_6k <- resample_cifti(
  cifti_fnames["dtseries"], resampled_xii_fname,
  resamp_res = 2000,
  surfL_fname, surfR_fname,
  resampled_surfL_fname, resampled_surfR_fname,
  write_dir = out_dir
)

## Separating CIFTI file.
## Time difference of 4.284612 secs
## Resampling CIFTI file.
## Time difference of 7.722892 secs
## Merging components into a CIFTI file... 
## Time difference of 0.1889989 secs

Resampled CIFTI

Resampling can also be performed while reading a file into R.

read_cifti(cifti_fnames["dscalar"], resamp_res=2000)

## Brain Structures: left cortex, right cortex  
##  left cortex: 1856 surface vertices, 2 measurements.
##  right cortex: 1863 surface vertices, 2 measurements.
## Intent: 3006 (dscalar).

Smoothing

Use smooth_cifti to perform smoothing. This function works for both CIFTI files and "xifti" objects.

smoothed_xii_fname <- "my_smoothed_cifti.dtseries.nii"
smooth_cifti(
  cifti_fnames["dtseries"], file.path(out_dir, smoothed_xii_fname),
  surf_FWHM=2, vol_FWHM=2,
  surfL_fname=surfL_fname, surfR_fname=surfR_fname,
  subcortical_zeroes_as_NA=TRUE
)
# Demonstrating the ability to use RColorBrewer palettes!
plot(
  read_cifti(file.path(out_dir, smoothed_xii_fname)), 
  surfL=surfL_fname, surfR=surfR_fname, 
  zlim=c(1,2), color_mode="diverging", colors="Spectral"
)

Smoothed CIFTI